Selection by Committee: SSL techniques work well when the model is able to provide an accurate prediction on unlabeled data. However, when this is not the case, SSL can have a detrimental effect to the overall system, since the model could be creating SSL data that is annotated incorrectly. Ideally, we would like to have a way of detecting when this might be the case.
Typically, for a given input \(x\) , neural networks provide a point estimate that is interpreted as a probability distribution over labels. If the point \(x\)  is easy to learn, neural networks trained from different initial conditions will learn a similar probability distribution for \(x\) . On the other hand, if \(x\)  is difficult to learn, their predictions are likely to disagree or converge to low confidence predictions. This phenomenon has been observed in several works addressing uncertainty estimation [1]}, [2]}. As a consequence, data points with high uncertainty are more likely to be incorrectly predicted than those with low uncertainty.
